Multi-resource Optimized Smart Management of Urban Energy
Infrastructures for Improving Smart City Energy Efficiency
Massimo Bertoncini
R&D General Direction, Engineering Ingegneria Informatica, Via Riccardo Morandi 32, Roma, Italy
Keywords: Smart Urban Infrastructures, Smart Cities, Smart Energy Grids, ICT-based Smart Monitoring and
Supervisory Control, Multi-utility Business Models.
Abstract: This paper presents an innovative conceptual, technological and business framework for an energy-centred
multi-resource optimized hub aimed at smart city infrastructure improved integration and improved energy
efficiency and reduced carbon footprints. Early results show a cost-effective operation for the integration
hub, as well as a 30% smart city/district energy efficiency overall increase when a synergistic operation
management is carried out for those resource supply infrastructures which are integrated through the
implemented hubs. Two major technological implementations have been presented and their current state of
implementation has been discussed, the first one aimed at integrating smart electricity grid with hydrogen
mobility infrastructure, while the latter one refers to a smart data centre hub interfacing with smart energy
(both power and heat) grids and with telco networks.
1 INTRODUCTION
This paper presents an innovative conceptual,
technological and business framework for an
energy-centred multi-resource optimized hub aimed
at smart city infrastructure improved integration and
improved energy efficiency and reduced carbon
footprints. Two major technological
implementations for this framework have been
presented and their current state of implementation
has been discussed, the first one aimed at integrating
smart electricity grid with hydrogen mobility
infrastructure (and/or with natural gas grid), the
latter one which refers to a smart data centre hub
interfacing with smart energy (both power and heat)
grids and with telco networks.
2 BACKGROUND AND
MOTIVATION
Efficient management of urban infrastructure
networks is becoming nowadays the cornerstone to
deal with for achieving significant energy efficient
gains and reducing carbon footprints in urban
environments, when we face with larger penetration
of fluctuating decentralized renewable energy
generation.
Infrastructure is commonly referred to as the
physical networks of energy supply, water,
communication, transportation, and waste removal
and treatment, including sewage. The current
infrastructure operation consists of separate supply
systems provisioning unconstrained demand, which
is considered as the societal need to deal with
(infinite resource mode).
Urban networks include:
Energy distribution networks (smart
electricity grid, district heating/cooling, gas
grid), whose main purpose is to transport
energy in whatever form from the
generation to the end user delivery point,
while consuming themselves energy.
Beyond-energy Resource distribution
networks (ICT, water, sewage, fuels as
energy carriers aimed at mobility/transport,
transport, including railways,
motorways,…) which consume Energy to
distribute/transport resources (data, water,
wastes, people)
Major focus is on urban energy systems
optimized management and their optimized
interaction with other resource (beyond energy)
infrastructures to achieve system-level energy
107
Bertoncini M..
Multi-resource Optimized Smart Management of Urban Energy Infrastructures for Improving Smart City Energy Efficiency.
DOI: 10.5220/0005499001070114
In Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS-2015), pages 107-114
ISBN: 978-989-758-105-2
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
efficiency. Urban energy systems have been defined
as the combined process of acquiring and using
energy to satisfy the demands of a given urban area
(Keirstead, 2013).
Latest advancements of ICTs have made possible
today to design and operate in a optimized way
individual energy distribution networks, which
include smart electricity grids, district
heating/cooling networks, gas grids, and beyond-
energy resource distribution networks, like ICT,
transport /fueling networks.
The state of the art for Infrastructure integration
implementation is for:
Different limited-scale pilot applications have
been implemented all over Europe (if not world)
No synergies in place at any level among the
different functional domains or infrastructures,
which is the main reason for:
Ongoing applications are tailored to
optimize resource efficiency in a single
domain (i.e. either energy, either
transport/mobility, either water, …)
Energy and resource-efficient district
planning and retrofitting are very seldom
cost-effective and very rarely citizens are
fully engaged in decision making process
Municipalities look at infrastructure networks as
individual and unrelated systems, producing
resource usage inefficiencies
Governance is based on unconstrained growing
demand results in inefficient and unsustainable
Current design and operation do not integrate
end users, in terms of the heterogeneity of their
energy and other resources demand and
behaviors (e.g. car ownership as unique
transport mode) and their crucial role in
selecting and using technological options (e.g.
selection and appropriate operation of energy
efficient technologies);
Disjoint and parallel delivery of different
infrastructure streams prohibits the development
of potential joint solutions (e.g. co-treatment of
waste and wastewater), or even substitutions
(e.g. substitution of electricity with gas through
micro-combined heat and power (CHP)),
between infrastructure systems. These
characteristics represent major barriers to
technical innovation and longer term
sustainability.
However the urban landscape nowadays is
rapidly moving forward towards an overall
sustainable system with embedded local energy
generation from Renewable Energy Sources (power
from PV, thermal energy from solar collectors) to be
exploited, used and/or integrated in whatever form
in the energy distribution networks aimed at
reducing energy intensity of human activities and
increase the smart city energy efficiency. The
emerging paradigm is for a constrained demand, in
which the basic assumption is the “limited natural
resources availability”, which need to be exploited at
largest possible extent when and where they are
available and suitably managed.
Emerging ecological models represent cities as
living self-sustainable organisms which pass trough
different life stages, each of them with different
level of energy consumption (and materials) to carry
out human processes (Urban Metabolism).
Leveraging on urban metabolism sustainable
models, cities may take on a key role in nurturing an
innovative smart sustainable development model,
aimed at activating a circular economy through a
synergistic approach, combining the city economic,
logistic and industrial activities with citizens quality
of life and overall energy efficiency and carbon
footprint reduction.
Innovation can be stimulated by regarding cities
as living organisms, with the continuous flow of
inputs and outputs as their “metabolism.” More
circular urban metabolism that treats outputs from
one use as inputs to another would help cities
increase resource productivity and adapt to a future
of resource limitations and climate uncertainty.
Self-sustainability for cities means a “system-of-
system” vision in which the objective is to consume
locally generated resources as much as possible,
with a view to reduce the energy cost necessary for
transporting locally generated resources elsewhere.
As such, different synergies may be found within
energy consuming (transport, ICT) and energy
transporting infrastructures (smart energy networks)
stakeholders, ranging from planning stage (Co-
Planning of a district-level decentralized CHP
generation system to couple heat and power system),
to the construction (es. digging sharing for telco and
power networks), operation phase (es. sharing assets
and facility among street lighting, telco networks,
information access and electric vehicle recharging,
billing IT systems sharing, coupled optimized
operation) and at level of service delivery and
business model (integrated services built upon data-
level cross-resource interoperability, without any
operational optimized multi-resource management,
multi-utility value proposition like providing the
requested thermal comfort level to the households).
Despite there have been a number of attempts for
designing and operating multi-carrier systems, the
large majority of these approaches are limited to
SMARTGREENS2015-4thInternationalConferenceonSmartCitiesandGreenICTSystems
108
deliver smart integration hubs for connecting energy
networks or, where other resource infrastructures are
considered, no systematic economic analysis is
carried out within a real multi-utility integrated
environment, nor any comprehensive modeling
which would include energy usage-driven
integration of beyond-energy resource supply
infrastructures has been conceived (Bruno et al.,
2014), (Geidl et al., 2007), (Hemmes et al., 2007),
(Maroufmashat et al., 2014), (Lehnhoff et al., 2013).
The urban landscape is characterized more and more
by rising local (fluctuating) renewable energy
generation, which aims at securing local energy
supply at lower costs and reducing the energy
intensity of natural resource usage. Such
proliferation of decentralized renewable energy
sources however would require increasing
decentralized flexibility to deal with local technical
constraints preventing energy (smart electricity grids
and district heating) and beyond-energy networks to
integrate larger shares of RESs. Accordingly
decentralized flexibility becomes the key resource
for instantiating the urban metabolism paradigm and
the maximization of available natural resource
potential deal with the societal need to fully exploit
the local available energy generation potential, while
reducing at the same time the cost of the investments
for local energy generation. Along this roadmap an
holistic design for coupled and interconnected
operation of energy and beyond-energy
infrastructures would rather allow to achieve larger
system-level efficiency, while reducing energy and
natural resource intensity of human activities. On the
other hand end users, as final consumers of energy
and other energy-consuming resources can play a
significant role to reduce pressure on the energy and
natural resource demand, through raising awareness
and suitable education towards adopting more
sustainable behaviors, which could bring significant
overall energy and natural resource saving and better
management. Humans behaviors and citizens
potential contribution to a more sustainable
development can be modeled as a further specific
network which will be interconnecting with energy
and other resource infrastructure in physical hubs
where humans consume energy to carry out their
own daily work or leisure activities. In particular
citizens will be consuming energy at home for
cooking, heating, watching TV, washing, will be
consuming energy for the water they consume and
finally they consume energy on the move by taking
train (transport infrastructure consuming energy).
3 THE APPROACH
We propose to model the smart city as a multi-
layered intertwined and coupled network
infrastructures, consisting of energy and other
resource (energy-consuming) infrastructures (like
ICT, water, transport) and human networks which
will be interacting and integrating one each other in
local multi-network cyber-physical hubs, where
either energy will be generated, either converted in
whatever other energy form or other resource, with a
view to secure and maximize local energy supply
through the provisioning of the requested levels of
flexibility. Suitable combinations of leading-edge
ICT technologies, like near real time sensing and
monitoring, intelligent processing, big data on line
analytics may be enabling the holistic optimized
operation of integrated coupled energy and other
resources infrastructures with human networks with
a view to achieve cost-effective optimal urban
system management and enhancing energy
efficiency, while reducing carbon footprint.
Decentralized flexibility could be provided by:
demand-side management strategies (like
load/demand flexibilization and active
energy consumers/prosumers engagement)
supply-side management (local
decentralized energy storage and/or
integration with other energy or resource
networks)
a combination of the above
It is worth to be said here that the integration of
locally generated energy should be carried out
through cost-effective integration in coupled energy
infrastructures, complemented by local storage and
active end users engagement for reducing their
energy consumption and actively contributing to
load flexibilization. In our model that non-energy
infrastructures, like communication, transport and
water ones could be used as further potential
integration networks with a view to provide
increased level of flexibility. The key assumption for
our Integrated Sustainable Urban Model is for Smart
Urban Infrastructures consisting of the application of
recent advances in real time smart monitoring and
processing technologies to support the optimized yet
flexible operation of the energy and beyond
networks (power, gas, district heating, water, ICT,
transport, waste...).
The second dimension along which smart city is
represented and implemented is the self-sustainable
system-of-systems. The vision is for future urban
systems moving away from traditional broadband-
Multi-resourceOptimizedSmartManagementofUrbanEnergyInfrastructuresforImprovingSmartCityEnergyEfficiency
109
like systems towards multi-directional networked
cyber-physical systems of systems, consisting of
smart decentralized multi-layered self-sustainable
sub-systems which: (i) optimize the consumption of
self-generated energy by distributed RES; (ii)
exchange and make available the resulting surplus of
energy to the upper connected layers (smart
building, smart district, smart
city/region/community); (iii) manage and solve
locally (at district/city levels) the major operational
challenges, reducing value chain length and costs
(es. Sustainable agriculture, zero waste, local energy
supply systems integrating fluctuating RES locally
at the largest possible extent with a view to reduce
transmission losses); (iv) are managed in a
coordinated and integrated way, with a view to
achieve holistic and synergistic resource efficient
distribution (optimized management of multi-carrier
energy networks, like Power2Gas systems); (v) are
enabled by ICT proactive systems, which include
finer-grained situational awareness and networked
optimized control; (vi) include in the loop at the
largest possible extents citizens, which are expected
to become “socially educated” sensors (nodes).
Smart cities representation hence is for a cyber-
physical Systems of Systems, consisting of large
interconnected partly autonomous sub-systems,
coupled by flows of electric power, steam, gas, and
other resources, where larger local RES generation
capability requires increased local-level network
flexibility solutions (demand-response, storage,
multi-utility integration…), which may be
effectively achieved through a local control and
decision-making, within the framework of a global
large-scale coordinated networked control. In a
nutshell our model aims at originally leveraging on
synergies among resource infrastructures (resource
supply side), while locally addressing flexible yet
optimized energy consumption as core innovation to
deploy cost effective Sustainable Smart Cities.
The expected outcome is to maximize synergies
among resource infrastructures to enable improved
systemic energy efficiency and optimal energy
management at district/city level within a multi-
resource synergistic management.
3.1 Technology Implementation
Within the proposed model we have developed a
suitable IT architecture for a decentralized cyber-
physical urban hub at the interplay among the
different networks. Such node will be in charge for
optimally manage and efficiently operate in real
time with a view either
to optimize a hub-level operational efficiency
(for example maximization of usage of locally
generated energy from RES, either power from
PVs either thermal energy from solar collectors
or micro-CHP, i.e. follow-the-sun strategy) or
economic criteria (multi-energy generation)
either to adapt real time hub operation with a
view to achieve given set points within a multi-
networks or coupled network optimization
(multi-network optimization) paradigm.
3.2 Business Models and Innovation
To achieve long-term sustainability infrastructure
needs to be designed and operated with the goal of
providing essential service delivery at radically
decreased levels of resource. This requires a new
approach based on a more systemic view of the
purpose of infrastructure.
This new approach will need to:
be focused on the service provided (thermal
comfort) rather than carrier supply (e.g. gas);
incorporate the end-user, in terms of their
energy and other energy-consuming resources
demand, behaviors and technological choices;
and above all will be enabled by recent
advancements in smart Information and
Communication Technologies (ICTs) able to
provide tighter real time situational awareness
and more effective capabilities for intelligent
processing of large amount of data to integrate
the operation of different infrastructures
The transition of urban infrastructure operation is
moving away from supply of unconstrained demand
and towards resource-efficient service delivery. This
requires a fundamental change in the business model
used to deliver effective infrastructure services. The
current throughput based approach, where profit is
made by increasing unit sales or utility products, is
not longer sustainable and vulnerable to resource
scarcity. It needs to turn on into a model where the
provided service and infrastructure companies are
incentivized to provide service at lowest possible
resource use. In addition we need to exploit the
benefits of infrastructure integration because one
service is often provided by multiple utilities. New
stakeholders are expected to emerge which can play
the role of integrated infrastructure service
companies (Multi-Resource Service Companies)
In order to better identify potential suitable
service delivery and appropriate business models,
we need to identify different yet increasing levels of
integrations and synergies among the urban resource
infrastructures:
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Integration of Facilities and Assets at
construction and operation stage to avoid the
underutilization of valuable fixed assets by
integrating facilities conventionally separated
by sectorial functions (sharing digging and
construction material during construction,
sharing equipment and/or facilities
Integration of Services across Silos (Business or
Service Integration) to break existing
organizational barriers and provide cross-
infrastructures service (Integrated Service
Delivery Model). Here Integrated service
delivery model is based on the IT cross-
infrastructure interoperability and integration
(es. Energy footprint of mobility patterns)
System-level Integration (Technical
Integration), where energy generation, resource
supply and waste management are integrated
either at design either at operational stage by
establishing cascades and system-level cost-
effective optimized multi-resource operation
The way in which innovation can be deployed at
smart city level and the respective service delivery
and business models will be strictly depending from
the tailored integration level.
Despite in principle the proposed framework can
instantiate whatever level of integration, the main
purpose of our proposed framework is to tailor the
third and highest level of integration, with a view to
maximize energy efficiency of human activities at
smart city level. In that respect integrated service
delivery model and a multi-utility model, enabled by
a system-level technical integration.
4 IMPLEMENTATIONS
At the current stage we carried out a number of
preliminary implementations for the proposed
infrastructure integration model, where we have
delivered an instantiation of the hub IT architecture
and a preliminary business analysis aimed at
demonstrating the cost-effective operation of the
integration hub, while considering the additional
costs incurred due to the technology stack
implementation (in terms of return on investments).
Furthermore from the early calculations we
conducted we demonstrated the 30% improved
efficiency (and related overall energy saving) for
synergistic management of resource supply
infrastructures against a disjoint separated operation
of the same infrastructures.
It is worth to be said that these implementations
presents different levels of maturity: while for the
case of INGRID we have already developed a
preliminary business case for the storage-enhanced
integration hub, in the case of the GEYSER project,
we are about to deliver the early prototype for a
smart integration hub able to couple energy and
telco infrastructures. The following implementations
have been partially developed so far: (i) Integrating
smart electricity grids with gas grid and/or with
hydrogen-based fuelling infrastructure for green
mobility through Hydrogen Energy Storage
(INGRID); (ii) Integrating smart energy grids and
telco networks through net-zero energy smart Data
Centres (GEYSER)
4.1 INGRID Implementation
Within the framework of the ongoing European FP7
INGRID project, a multi-network optimized
management of a hydrogen energy storage
enhanced-hub has been developed at the interplay
between smart electricity grid and natural gas grid
and/or green mobility infrastructure, whereas the
surplus of hydrogen will be used as fuel for
alternative mobility or in alternative can be injective
in the nearby natural gas network.
INGRID (High-capacity hydrogen-based green-
energy storage solutions for grid balancing) is a
European R&D ongoing project which aims at
demonstrating in a real life operational context the
technical and economic feasibility of decentralized
solid hydrogen energy storage supporting the joint
optimized operation of a multi-carrier energy
distribution systems, which includes the
simultaneous optimal (technical and/or economic
management) of electricity and hydrogen
infrastructures. Main challenge is to make use of
smart grids technologies for real time monitoring
and optimized control combined with solid-state
hydrogen energy storage systems for effectively
balancing power demand and supply in the Medium
and Low Voltage branches of the power network in
a scenario of large (>25-30%) penetration of
intermittent distributed Renewable Energy Sources
at smart district level. Hydrogen energy storage
coupled with distributed RES generation and onsite
hydrogen use and/or nearby transportation may
enable multi-utility systems to be efficiently and
sustainably used to integrate larger shares of RESs
in the overall energy distribution system, with a
concrete increase in resource efficiency distribution,
yet reducing the problem of the curtailment of the
generation facilities to prevent unsecure power
network operations.
Multi-resourceOptimizedSmartManagementofUrbanEnergyInfrastructuresforImprovingSmartCityEnergyEfficiency
111
Figure 1: INGRID Implementation of the proposed model.
In that respect we have been developed a novel ICT-
based cooperative storage-based inter-grid energy
management and control strategies for the optimal
behavior of the storage hub at the interface between
multiple energy grids (INGRID Energy Management
System), and respective stakeholders (Distribution
System Operators, storage operators/service
providers and hydrogen value chain operators
(including natural gas distributors).
EMS will be deployed in a field demonstrator
(actually in commissioning stage), which consists of
a Water Electrolyser (WE), an Hydrogen Solid
Storage (HSS) system, a Fuel Cell (FC), an internal
RES based plant and an Electric Vehicle Recharge
Station. The INGRID system is connected to the MV
branch of the power network insisting on the Troia
(Foggia, Italy) Primary Substation 150/20 KV,
where a significant intermittent generation from PV
and wind farms will be converging Two different
families of Optimized Management. INGRID EMS
will be operated through different optimization
policies, which implies different business models:
(i) Multi-network operation, where the main
objective will be to support local Power Distributors
in Lower Voltage Grid stabilization by provisioning
of network services. Here the local DSO will play an
“incumbent” role, by defining the near real time set
points for the storage-enhanced hub, which will be
accordingly in charge for self-adapting their internal
hydrogen surplus production and deployment. Local
DSO-level electricity balancing markets can be
implemented, where local DSOs can eventually
procure ancillary services for local grid stabilization,
while green hydrogen surplus can be locally used or
transported for subsequent industrial or other uses;
(ii) Multi-generation, where hub-level multi-
energy optimization will be carried out, enhanced by
hydrogen energy storage, aimed at maximizing the
local hub district-level economic result, based on the
near real time monitoring input from local RES
generation and on the economical stimuli from the
smart power grid and from hydrogen market (or gas
grid). Underlying business models will be based on:
(i) local Third Party Independent Storage Service
Operator within a Merchant Ownership Model and
(ii) bundling different storage services with a view
to achieve double reward for service deployment at
Medium and Low Voltage levels, while reducing the
high CAPEX for storage facility deployment. Early
simulations shows a positive business case within
five-six years, for a given bundling configuration.
However more detailed analysis for a more
extensive range of bundling configurations will be
carried out during the demonstrator operation.
4.2 GEYSER Implementation
An implementation for a Net-zero Energy Data
Center acting as energy-driven multi-resource hub at
the interplay between Smart energy grids (either
smart power grid either district heating or both) and
telecommunication network for workload shifting
has been developed within the GEYSER project.
Main background for this implementation is that
no active links among Data Centers/ICT networks
and Smart Cities and its respective energy utilities
(either electricity either heating operators) and no
energy or info exchange do exist among them and
urban Data Centers are operated in uncoordinated
way and their Energy efficiency has been so far
addressed in an isolated way. However urban Data
Centres have a large yet mostly unexploited
flexibility potential to contribute to smart city local
energy consumption optimization and smart energy
grids optimized operation, at the extent of IT
workload time and spatial migration through
telecommunication networks. Our implementation
provides a Resource Management System for a net-
zero energy Data Center hub, which will be
conveniently integrated with Smart City level
Energy operators. Along such vision, data centers
are expected to turn on into flexible energy players
at the crossroads of Smart City and Smart Energy
Grids, with a view to become adjustable adaptive
power consumers. We can identify the GEYSER
unique selling proposition as near real time energy
flexibility service provisioning at local level, aimed
at either alleviating power grid local (MV and LV)
network constraints in real time caused by the
imbalances derived by surplus/shortage of electricity
generated by stochastic RESs, either optimizing
nearby city/district level energy management with a
view to prioritize green local produced energy.
GEYSER builds its flexibility offer to the interested
stakeholders (local DSOs, retailers, ESCOs, energy
traders) though uniquely combining and optimizing
SMARTGREENS2015-4thInternationalConferenceonSmartCitiesandGreenICTSystems
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internal (back up and intermittent renewable)
generation capability with internal energy (either
thermal either electric) storage and computational
workload shifting techniques. In particular, local
power generation and thermal storage (i.e. DER
management) combined with workload management
has a significant potential to shed the peak load and
reduce energy costs, provided that adaptation,
responsiveness and latency time will be matching
with real time local utility operator requirements.
The unique combination of optimal internal
back-up generation, with thermal storage and
computational workload (temporal and
geographical) shifting allow next generation Data
Centers (i.e. “GEYSER-enabled Data centres”) to
become active stakeholders in the energy market and
smart city/local energy optimization affair.
GEYSER DC flexibility offering will be conveyed
and made available through local green energy
marketplaces tailored to energy sellers/traders,
ESCOs, municipalities for anticipated energy
contracting and provisioning (energy services in
time-ahead markets). Main stakeholder here is the
smart city /district energy manager who will be in
charge for procuring energy (in all the necessary
forms and with an economically optimized portfolio
of energy carriers/mix (power, heat, gas,…). Here
both a multi-network optimization can be carried out
if Smart City Energy Manager will place a bid on
the scheduling marketplace (es. One day/hour ahead)
for the energy mix requirement, otherwise a multi-
generation scenario can be operated where the data-
center hub will adapt its internal computational
workload and local generation and storage with a
view to achieve the best economic results.
Eventually follow-the-sun strategy could be
conveniently adopted by the data centers if suitable
economic signals will be captured from the city-
level DSO or district heating operator. For example,
follow-the sun strategy local generation combined
with thermal storage could be used for temporary
thermal storage with a view to subsequently
alleviating the later thermal network peak demand.
Otherwise Data Centers could be directly
participating to Local Balancing Marketplaces by
Network Service real time provisioning to alleviate
their specific operational problems. For example
data centers could provide flexibility services to
DSO to manage peak shaving in the DSO network,
provide a firm load diagram (load leveling), or
provide local balancing service systems like voltage
regulation or reactive power regulation.
Suitable business models have been specifically
designed for Smart City viable value proposition,
Figure 2: GEYSER Implementation of the proposed
model.
based on Time based Service Level Agreements,
intended as major or minor responsiveness of DCs to
offer the required flexibility (by uniquely combining
local generation and storage with computational
workload shifting) with different latency times. For
instance, a Data Centre may deploy a Premium,
more expensive, service with a very low latency
time and highest time responsiveness in the range of
1 to 5 minutes. Otherwise, we can imagine cheaper
less responsive ancillary services tailored to DSOs in
the range of 5 to 15 minutes.
5 CONCLUSIONS
An innovative model for energy-led smart urban
resource infrastructure model has been proposed,
which includes a conceptual, a technological and a
business layer implementation. Two major
implementations within the on-going FP7 European
projects INGRID and GEYSER have been
illustrated, despite they are at different maturity
stages, which demonstrate the cost-effective
operation of a resource management hub enhanced
with storage, with a view to demonstrate cost
effectiveness and improved energy efficiency of
smart city when resource supply infrastructures are
operated in a coupled way.
REFERENCES
Bruno, S., Dassisti, M., La Scala, M., Chimienti, M.,
Stigliano, G., Palmisani, E., 2014, Managing
Networked Hybrid-Energy Systems: a Predictive
Dispatch Approach, 19th World Congress of The
International Federation of Automatic Control, Cape
Town, South Africa. August 24-29.
GEYSER, http://www.geyser-project.eu/
Multi-resourceOptimizedSmartManagementofUrbanEnergyInfrastructuresforImprovingSmartCityEnergyEfficiency
113
Geidl, M., Koeppel, G., Favre-Perrod, P, Klöckl, B.,
Andersson1, G., Fröhlich, K., 2007, The Energy Hub –
A Powerful Concept for Future Energy Systems, Third
Annual Carnegie Mellon Conference on the Electricity
Industry, 13 – 14 March.
Hemmes, K., Zachariah-Wolff, J., Geidl, M., Andersson,
G., 2007, Towards multi-source multi-product energy
systems, International Journal of Hydrogen Energy
32, pag. 1332 – 1338.
INGRID, http://www.ingridproject.eu.
Keirstead, 2013, in Urban Energy Systems, edited by
Keirstad, J., Shah, N.
Maroufmashat, A., Khavas, S., Bakhteeyar, H.,2014,
Design and Operation of a Multicarrier Energy System
Based On Multi Objective Optimization Approach,
World Academy of Science, Engineering and
Technology, International Journal of Environmental,
Ecological, Geological and Marine Engineering.
Lehnhoff, S., Rohjans, S., Appelrath, H., 2013, ICT-
challenges in Load Balancing across Multi-Domain
Hybrid Energy Infrastructures, Information
Technology, 55(2013)/DOI 10.1.1524.
SMARTGREENS2015-4thInternationalConferenceonSmartCitiesandGreenICTSystems
114
